• IMMUNE NETWORK
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• v.14 no.1
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• pp.1-6
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• 2014

Epstein-Barr virus (EBV) is etiologically associated with a variety of diseases including lymphoproliferative diseases, lymphomas, carcinomas, and autoimmune diseases. Humans are the only natural host of EBV and limited species of new-world monkeys can be infected with the virus in experimental conditions. Small animal models of EBV infection, required for evaluation of novel therapies and vaccines for EBV-associated diseases, have not been available. Recently the development of severely immunodeficient mouse strains enabled production of humanized mice in which human immune system components are reconstituted and express their normal functions. Humanized mice can serve as infection models for human-specific viruses such as EBV that target cells of the immune system. This review summarizes recent studies by the author's group addressing reproduction of EBV infection and pathogenesis in humanized mice.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.1 s.190
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• pp.93-100
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• 2007

In this study, an investigation was made on bristles for the prosthetic and periodontally involved patients based on the previous studies. The purpose of this study was to investigate the ideal location and dimensions of bristles for the patients who were installed with fixed prostheses and severely involved periodontitis. 3D scanner was used to develop 3D visual models of bristles and teeth. These models were developed to be used for designing the ideal bristles for the prosthetic and periodontally involved patients. For the ideal design of bristle, interproximal area of dental arch and bristle must get into maximum and standard teeth may have to be chosen from many gypsum molds. During the design process the factors should be considered that influence the removal of plaque by bristle.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.759-771
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• 2005

Twin studies are one of the most widely used methods for quantifying the influence of genetic and environmental factors on some traits such as a life span or a disease. In this paper we propose a genetic mixed linear model for twin survival time data, which allows us to separate the genetic component from the environmental component. Inferences are based upon the hierarchical likelihood (h-likelihood), which provides a statistically efficient and simple unified framework for various random-effect models. We also propose a simple and fast computation method for analyzing a large data set on twin survival study. The new method is illustrated to the survival data in Swedish Twin Registry. A simulation study is carried out to evaluate the performance.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.51-56
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• 2000

When the standard inference is to be used with complete data and nonresponse is ignorable, then multiple imputations should be created as repetitions under a Bayesian normal model. Many Bayesian models besides the normal, however, approximately yield the standard inference with complete data and thus many such models can be used to create proper imputations. We consider the Bayesian bootstrap (BB) application.

• Journal of Integrative Natural Science
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• v.8 no.3
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• pp.176-183
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• 2015

Corticotropin-releasing actor receptors (CRFRs) activates the hypothalamic pituitary adrenal axis, one of the 2 parts of the fight or flight response to stress. Increased CRH production has is associated with Alzheimer's disease and major depression and hypoglycemia. In this study, we report the important structural and chemical parameters for CRFR inhibitors using the derivatives of 8-substituted-2-aryl-5-alkylaminoquinolines. A 3D QSAR study, Comparative molecular field analysis (CoMFA) was performed. The best predictions were obtained for the best CoMFA model with a $q^2$ of 0.607 with 6 components and $r^2$ of 0.991. The statistical parameters from the generated CoMFA models indicated that the data are well fitted and have high predictive ability. The contour map resulted from the CoMFA models might be helpful in the future designing of novel and more potent CRFR derivatives.

• Communications for Statistical Applications and Methods
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• v.25 no.4
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• pp.385-396
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• 2018

Competing risks are commonly encountered in biomedical research. Regression models for competing risks data can be developed based on data routinely collected in hospitals or general practices. However, these data sets usually contain the covariate missing values. To overcome this problem, multiple imputation is often used to fit regression models under a MAR assumption. Here, we introduce a multivariate imputation in a chained equations algorithm to deal with competing risks survival data. Using pseudo-observations, we make use of the available outcome information by accommodating the competing risk structure. Lastly, we illustrate the practical advantages of our approach using simulations and two data examples from a coronary artery disease data and hepatocellular carcinoma data.

• BMB Reports
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• v.48 no.10
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• pp.541-548
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• 2015

Cardiomyopathy is an inherited or acquired disease of the myocardium, which can result in severe ventricular dysfunction. Mitochondrial dysfunction is involved in the pathological process of cardiomyopathy. Many dysfunctions in cardiac mitochondria are consequences of mutations in nuclear or mitochondrial DNA followed by alterations in transcriptional regulation, mitochondrial protein function, and mitochondrial dynamics and energetics, presenting with associated multisystem mitochondrial disorders. To ensure correct diagnosis and optimal management of mitochondrial dysfunction in cardiomyopathy caused by multiple pathogenesis, multidisciplinary approaches are required, and to integrate between clinical and basic sciences, ideal translational models are needed. In this review, we will focus on experimental models to provide insights into basic mitochondrial physiology and detailed underlying mechanisms of cardiomyopathy and current mitochondria-targeted therapies for cardiomyopathy.

• Journal of Biomedical Engineering Research
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• v.27 no.4
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• pp.180-188
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• 2006

Bristles for the prosthetic and periodontally involved patients are investigated based on previous studies. Specifically, the ideal location and dimensions of bristles for the patients who were installed with fixed prostheses and severely involved periodontitis were investigated. 3D (Three-Dimensional) scanner was used to develop 3D visual models of bristles and teeth. These models were used for designing the ideal bristles. In the design, the interproximal area of dental arch and bristle must be maximized and the standard teeth may have to be chosen from many gypsum molds. During the design process the factors that influence plaque removal by the bristle were considered.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.223-229
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• 2021

Classification of different blood cell types is an essential task for human's medical treatment. The white blood cells have different types of cells. Counting total White Blood Cells (WBC) and differential of the WBC types are required by the physicians to diagnose the disease correctly. This paper used transfer learning methods to the pre-trained deep learning models to classify different WBCs. The best pre-trained model was Inception ResNetV2 with Adam optimizer that produced classification accuracy of 98.4% for the dataset comprising four types of WBCs.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.2
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• pp.59-65
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• 2023

Currently, polysomnography is essential to diagnose sleep-related breathing disorders. However, there are several disadvantages to polysomnography, such as the requirement for multiple sensors and a long reading time. In this paper, we propose a system for predicting the severity of sleep-related breathing disorders at home utilizing measurable elements in a wearable device. To predict severity, the variables were refined through a three-step variable selection process, and the refined variables were used as inputs into three machine-learning models. As a result of the study, random forest models showed excellent prediction performance throughout. The best performance of the model in terms of F1 scores for the three threshold criteria of 5, 15, and 30 classified as the AHI index was about 87.3%, 90.7%, and 90.8%, respectively, and the maximum performance of the model for the three threshold criteria classified as the RDI index was approx 79.8%, 90.2%, and 90.1%, respectively.